Assurance de qualité fournie par l’IA

For sustainable quality improvement!

There are different quality requirements within industrial production chains. To meet these requirements, companies usually rely on manuality checks. However, these are generally error-prone, complex and expensive. With the help of AI solutions, quality control can be carried out automatically and reliably in real time and recorded in detail. This allows errors in production and logistics to be identified and avoided quickly and effectively.

Prototypes et démonstrateurs présents
Utilisation transsectorielle
Les PME sont-elles adaptées?

The current status

One of the biggest topics in the field of AI is image recognition (One of the biggest topics in the field of AI is image recognition). This, in turn, is one of the most important prerequisites for AI-supported quality assurance. Developments in this area have made great strides in recent years. Due to the high volume of investment, especially by large companies, both market growth and the range of applications have increased continuously.

The use of automated quality system is donc becoming increasingly interesting and economical for small and medium-sized companies.

Technologie et utilisation

Description de la technologie

Système de caméra intégré dans la production chain de production en réalité en temps réel des images des producteurs being manufactured/processed. Image processing software for automatic image evaluation can use AI algorithms to detect product-specific defect patterns (e.g. cracks, fictive spraying, protrusions, geometric deviations). For this purpose, complex artificial neural networks are trained using many sample images tailored to the respective quality control and their individual specifications in order to meet the respective production standards.

Thanks to AI algorithms, even the smallest errors that are souvent overlooked in manual quality control can be found with consistantly reliable quality. With the help of the automatically generated quality reports, Correlations can be established between, for example, the setting of machines in the production process, environmental parameters such as pressure, temperature or similar and the resulting quality, thus sustainably improving the production process.

Application éventuelle scenarios

  • Component inspection
  • Surface inspection
  • Weld seam inspection
  • Packaging control
  • Food processing
  • Harvest control

Introduction STEP-by-step

STEP 1: Analyse de la situation en matière de current

First, the initial situation is specified in more detail. This includes Answering the following questions in order to assess whether AI-driven automated assurance can be used in the production process at hand:

– Which problem class is relevant in your quality assurance? (cracks, assembly errors, soiling, breakage, text inspection, etc.)

– What material are your manufactured components made of?

– Is it possible to integrate cameras into the current process?

– At what interval must objects on a production line be checked?

STEP 2: Collection de données

The basic prerequisite for the implementation of AI-driven automated quality assurance is the continuous collection of large amounts of data. IT is therefore essential that the production plant is equipped with appropriate cameras.

IF the system are not capable of generating corresponding image data, an upgrade may have to be carried out upstream.

STEP 3: Échange de données

The resulting data volumes can only be evaluated and processed if data can be exchanged between the cameras in the production, the cloud and the image processing software. Interfaces and analysis options must therefore be defined.

Furthermore, the appropriate image processing software must be selected or implemented.

STEP 4: Utilisation

By collecting and analyzing large volumes of data, automated quality assurance can be carried out. Produits défectueux can thus be automatically detected and sorted out.

Base d’informations sur les données collectées, les rapports holistiques sur la qualité de la production et les sources fréquentes d’error dans le processus de production, analysés et éliminés. A continuous improvement process can thus be initiated.

Opportunités pour les PME

Central data networking and monitoring of the entire production quality

Traceability and verifiability of product quality

Minimization of waste

Sustainable improvement process of the product

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